Esta coleção reúne imagens ópticas adquiridas pelos sensores WFI (Wide Field Imager), embarcados nos satélites do INPE: CBERS-4, CBERS-4A e AMAZONIA-1. As imagens referem-se às regiões de Franca (SP), Franca Nordeste (SPNE) e partes da Amazônia brasileira, com destaque para o monitoramento de áreas naturais, queimadas, cobertura de nuvens e corpos hídricos. Os arquivos seguem um padrão de nomenclatura que codifica informações como a localidade (ex.: Franca_AMAZONIA, Franca_SP), o sensor utilizado, a data da imagem (AAAAMMDD), órbita / ponto, o nível de processamento (ex.: L4), composições espectrais (ex.: BAND4321, BAND16151413) e tipo de processamento (ex.: RegAWFI). Os dados estão disponíveis nos formatos .tif e .png.

CBERS-4/PAN5M - Level-4 Digital Number. L4 product provides orthorectified images.

CBERS-4/MUX - Level-2 Digital Number. Level 2 products have radiometric correction and geometric correction using satellite ephemeris and attitude data (system correction).

This is a land cover classification map of Brazilian Cerrado, from August 2017 to August 2018. This classification was made on top of Landsat-8 monthly cubes with spatial resolution of 30 meters, using the best pixel composition function named as Least Cloud Cover First (LCF), which was previously named Stack in BDC older versions. The input datacube was Landsat-8 - OLI - Cube Stack 16 days - v001, which was deprecated. The classification model was trained using 67359 sample points spread across the whole Cerrado biome (Annual Crop: 9390, Dune Beach: 35, Forest: 5439, Pasture: 19697, Savanna: 30014, Semi-Perennial Crop: 1161, Silviculture: 1268, Water: 355). The spectral band used were B1, B2, B3, B4, B5, B6, and B7 along with the vegetation indices EVI and NDVI; the clouded observation were identified using the Fmask4 algorithm and estimated using linear interpolation. The classification algorithm was Multi-Layer Perceptron (Deep Learning). This product was funded by the Brazilian Development Bank (BNDES).

This land cover classification refers to a study area in Mato Grosso state, in the Cerrado biome. For this map, the CBERS-4/WFI monthly data cube was used, with a spatial resolution of 64 meters, using the best pixel composition function named as Least Cloud Cover First (LCF), which was previously named Stack in BDC older versions. This experiment uses the time series of an agricultural calendar year, from September 2018 to August 2019, extracted from the CBERS-4/WFI data cube. The input datacube was CBERS-4 (WFI) Cube Identity - v001, which was deprecated. The classification was made using 852 samples (Annual Crop: 257; Natural Vegetation: 245; Pasture: 216; Semi-Perennial Crop: 134) and the following data cube bands: bands red, green, blue, and near-infrared along with the EVI, NDVI, GEMI, GNDVI, NDWI2, PVR indices. We trained a multi-layer perceptron for a deep learning classification network to classify the data cube using sits R package. This product was funded by the Brazilian Development Bank (BNDES).

AMAZONIA-1/WFI - Level-4 Digital Number product.

The MODIS-Aqua Monthly Remote Sensing Reflectance (Rrs, unit sr-1) provides 8 spectral bands temporal resolution of one month and spatial resolution of 1 km over the Brazil oceanic waters and open ocean South Atlantic waters. This collection captures 7 visible, and 1 infrared channels using Level-1A images acquired from the Moderate Resolution Imaging Spectroradiometer (MODIS) instrument onboard the NASA's Aqua satellite. The Level-1A data were processed into Level-1B and GEO using the Data Processing Tools (modis_L1B) from SeaWiFS Data Analysis System (SeaDAS) software. The Level-1B and GEO data were applied in the atmospheric correction OC-SMART (Fan et al., 2021) by the National Institute for Space Research (INPE, Brazil) and the Laboratoire d'Océanologie et de Géosciences (LOG, France) generating the Level-2 data. The Level-2 data has been mosaicked to generate daily maps capturing the complete Brazilian ocean waters. The daily mosaic data were reprojected to geographical (lat/lon) coordinates using as reference the European Space Agency (ESA) Ocean Colour - Climate Change Initiative (OC-CCI) into Level-3 grid. Both, the mosaic and the reprojection were done using the Sentinel Application Platform (SNAP) on its version 10. Finally, the daily reprojected data were temporal merged to create the monthly Rrs products using the geometric mean.

CBERS-4/PAN10M - Level-4 Digital Number. L4 product provides orthorectified images.

AMAZONIA-1/WFI - Level-4 Surface Reflectance product. L4 SR product provides orthorectified surface reflectance images. This dataset is provided as Cloud Optimized GeoTIFF (COG).

Sentinel-2 image mosaic of Brazilian Amazon biome with 10m of spatial resolution. The mosaic was prepared in support of TerraClass project. The false color composition is based on the MSI bands 11, 8A and 4 assigned to RGB channels. The temporal composition encompasses 03-months of images, starting in june and ending in August for the years 2022 and 2024, with a best pixel selection approach called Least Cloud Cover First (LCF). More information on LCF can be found at Brazil Data Cube web site (https://brazil-data-cube.github.io/specifications/processing-flow.html#temporal-compositing). This Image Mosaic used more than 30000 Sentinel-2 scenes and was generated based on an existing data cube of Sentinel-2 images.